---
title: "Action2Activity: Recognizing Complex Activities from Sensor Data"
authors:
  - "Ye Liu"
  - "Liqiang Nie"
  - "Lei Han"
  - "Luming Zhang"
  - "David S. Rosenblum"
doi: ""
url: "https://ijcai.org/proceedings/2015/231"
pdf: "https://ijcai.org/proceedings/2015/0231.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "1617-1622"
track: "Main Track"
keywords:
---

# Action2Activity: Recognizing Complex Activities from Sensor Data

Ye Liu, Liqiang Nie, Lei Han, Luming Zhang, David S. Rosenblum

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 1617-1622.

## Abstract

As compared to simple actions, activities are much more complex, but semantically consistent with a human's real life. Techniques for action recognition from sensor generated data are mature. However, there has been relatively little work on bridging the gap between actions and activities. To this end, this paper presents a novel approach for complex activity recognition comprising of two components. The first component is temporal pattern mining, which provides a mid-level feature representation for activities, encodes temporal relatedness among actions, and captures the intrinsic properties of activities. The second component is adaptive Multi-Task Learning, which captures relatedness among activities and selects discriminant features. Extensive experiments on a real-world dataset demonstrate the effectiveness of our work.